Case Study -A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company is experimenting with consecutive training jobs.How can the company MINIMIZE infrastructure startup times for these jobs?
Case Study -A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.Which solution will meet this requirement?
Case Study -A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.Which action will meet this requirement?
HOTSPOT -A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.• Store the resulting data back in Amazon S3.• Use Amazon Athena to infer the schemas and available columns.• Use AWS Glue crawlers to infer the schemas and available columns.• Use AWS Glue DataBrew for data cleaning and feature engineering.
HOTSPOT -An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)• Access the store to build datasets for training.• Create a feature group.• Ingest the records.
Case Study -A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.The company needs to use the central model registry to manage different versions of models in the application.Which action will meet this requirement with the LEAST operational overhead?
